2026-06-01T14:48:17Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/267432026-02-03T12:24:24Zcom_10630_2254col_10630_37959

Monitoring and detection of toothbrushing with smart watches and artificial intelligence. Casilari-Pérez, Eduardo González-Cañete, Francisco Javier Dientes - Cuidado e higiene - Equipo electrónico This work proposes to combine wearables and the use of artificial intelligence techniques to detect manual mobility patterns caused by brushing teeth. Specifically, the article describes and evaluate a system based on convolutional neural networks, able to identify brushing gestures from samples of few seconds of inertial accelerometry signals gathered by wrist-worn devices. The architecture is systematically trained and validated with the signals provided by different public databases which were collected when different experimental subjects executed different manual actions. The results show the effectiveness of the detector, since it reaches a sensitivity and specificity greater than 95% when applied to discriminate brushing from other hand actions. In addition, the system is re-trained and assessed with the real-life samples captured by a smartwatch, where the neural model could be implemented to operate and produce real-time decisions. 2023-06-02T08:52:47Z 2023-06-02T08:52:47Z 2023-06-02T08:52:47Z 2023 conference output https://hdl.handle.net/10630/26743 eng MQDS 2023 Measurement, Quality, Data Science Burdeos (Francia) 5-7 Junio, 2023 open access